AI and graph-based systems.
Affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. We have developed a graph-based systems simulator that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulation results are valid. You can learn more about this simulator on its Github site.
The Graphitti simulator supports investigations that require high-performance parallel simulation. It decreases the time needed to write parallel software while simultaneously providing mechanisms for validating results against serial implementation.
The Graphitti simulator is an evolution of the previous BrainGrid parallel neural simulator. The name "BrainGrid" came from a previous investigation of the use of agent-based middleware (AgentTeamwork) for this application.